Individualizing a monaural beamformer for cochlear implant users

Nogueira, Waldo; López, Marta; Rode, Thilo; Doclo, Simon; Buechner, Andreas

doi:10.1109/icassp.2015.7179071

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…These distortions and artifacts are audible for NH listeners and limit the applicability of these algorithms for re-mixing purposes. However, given that CI users seem to better tolerate audio distortions than NH listeners, 24 because they already receive a signal with reduced spectro-temporal resolution, SS algorithms could be a promising tool for remixing music for this population.…”

Section: Introductionmentioning

confidence: 99%

Remixing music using source separation algorithms to improve the musical experience of cochlear implant users

Pons

Janer

Rode³

et al. 2016

The Journal of the Acoustical Society of America

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Music perception remains rather poor for many Cochlear Implant (CI) users due to the users' deficient pitch perception. However, comprehensible vocals and simple music structures are well perceived by many CI users. In previous studies researchers re-mixed songs to make music more enjoyable for them, favoring the preferred music elements (vocals or beat) attenuating the others. However, mixing music requires the individually recorded tracks (multitracks) which are usually not accessible. To overcome this limitation, Source Separation (SS) techniques are proposed to estimate the multitracks. These estimated multitracks are further re-mixed to create more pleasant music for CI users. However, SS may introduce undesirable audible distortions and artifacts. Experiments conducted with CI users (N = 9) and normal hearing listeners (N = 9) show that CI users can have different mixing preferences than normal hearing listeners. Moreover, it is shown that CI users' mixing preferences are user dependent. It is also shown that SS methods can be successfully used to create preferred re-mixes although distortions and artifacts are present. Finally, CI users' preferences are used to propose a benchmark that defines the maximum acceptable levels of SS distortion and artifacts for two different mixes proposed by CI users.

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Section: Introductionmentioning

confidence: 99%

Remixing music using source separation algorithms to improve the musical experience of cochlear implant users

Pons

Janer

Rode³

et al. 2016

The Journal of the Acoustical Society of America

Self Cite

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“…For non-stationary noisy backgrounds, speech enhancement can be achieved by means of spatial filtering algorithms (i.e., beamformers), assuming that the target speech and masking noise are spatially separated [5,8]. Nonetheless, more recently, data-driven approaches based on deep neural networks (DNNs), have been also successful at improving speech understanding in non-stationary background noise conditions for CI listeners [9,10].…”

Section: Introductionmentioning

confidence: 99%

An End-to-End Deep Learning Speech Coding and Denoising Strategy for Cochlear Implants

Gajęcki

Nogueira

2021

Preprint

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Cochlear implant (CI) users struggle to understand speech in noisy conditions. In this work, we propose an end-to-end speech coding and denoising sound coding strategy that estimates the electrodograms from the raw audio captured by the microphone. We compared this approach to a classic Wiener filter and TasNet to assess its potential benefits in the context of electric hearing. The performance of the network is assessed by means of noise reduction performance (signal-to-noise-ratio improvement) and objective speech intelligibility measures. Furthermore, speech intelligibility was measured in 5 CI users to assess the potential benefits of each of the investigated algorithms. Results suggest that the speech performance of the tested group seemed to be equally good using our method compared to the front-end speech enhancement algorithm.

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An End-to-End Deep Learning Speech Coding and Denoising Strategy for Cochlear Implants

Gajęcki

Nogueira

2022

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Individualizing a monaural beamformer for cochlear implant users

Cited by 5 publications

References 17 publications

Remixing music using source separation algorithms to improve the musical experience of cochlear implant users

Remixing music using source separation algorithms to improve the musical experience of cochlear implant users

An End-to-End Deep Learning Speech Coding and Denoising Strategy for Cochlear Implants

An End-to-End Deep Learning Speech Coding and Denoising Strategy for Cochlear Implants

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